AI consulting · Clarksville, TN · working nationwide
An engagement here is not a report you take to another vendor. We look at where the hours go, decide which of that work AI should take on and which it should not, and write the plan. Then we build it ourselves and hand you a system that runs. One principal, start to finish.
Free first conversation. Replies typically within one business day.
Advisory work that ends in a working system. We find where the hours actually go, decide which of that work AI can take on safely and which it should not, and write the plan the build follows: the data it reads, the review gates, the logging, and what it will cost to run. The same team then builds it, so the plan was never written for someone else to interpret.
Most engagements settle on one of three kinds of work. Retrieval, where the answer already exists somewhere across your email, contracts or files, and the cost is the hour it takes a person to find it; the plan decides which sources to index and makes every answer cite the document it came from. Classification, where inbound arrives faster than anyone can sort it and a miss means a real deadline.
Third, standing work that runs for days rather than one prompt at a time, with memory of what it did yesterday. If you are not sure you need that, start with what an AI agent actually is, because most businesses asking for an agent need retrieval first. For each kind, the plan names the review gates, the logging, and where a person has to stay in the loop.
Operators who have decided AI should be doing something in the business and are not yet sure what. Sometimes that is a firm that tried ChatGPT, got a confident wrong answer, and wants to know whether the problem was the tool or the approach. Sometimes it is a team buried in email or intake that suspects a system could carry some of it.
Small businesses are in scope, and often the better fit. A ten-person company usually has one or two processes where AI pays off clearly and a long list where it does not, and knowing which is which is worth more than a pilot on the wrong one. If the honest answer is that AI is the wrong tool, that is what the plan says.
It is a poor fit if what you need is a strategy deck for a board, with no intention of building anything. That is a different service, and other firms do it well.
Mid-size law firm
17,000+
The hours were going into correspondence: case-relevant threads spread across attorneys' mailboxes and found by hand. Scoping settled what to index, how retention and access would work, and that every result had to cite its thread. Then the same team built it.
NOWCase-relevant correspondence resolves by search rather than by manual review. Results return with thread citations attorneys can open and verify.
Commercial real estate · HDS reference build
This one is our own reference build: the system behind an owner-operated commercial real estate practice, built and run by HDS.
NOWThe practice runs without a dashboard. Market briefings arrive before the day starts, pipeline and follow-up live in one system of record, and the agent fleet does the research a junior analyst would otherwise be hired for. This is the reference build for the pattern we sell: agents doing standing work, and a messaging app instead of a UI.
01 · ASSESS
A free conversation, then a look at the real work: the inbox, the files, the hours. You find out whether AI belongs here at all.
02 · PLAN
A written plan: which work, what data, where a person approves, and what it costs to build and to run.
03 · BUILD
The same team builds it. No handoff to another vendor, so nothing in the plan gets reinterpreted.
04 · VERIFY
Checked against your real cases before it goes live, with a log of everything it does.
Stage three is where this page hands over to the systems we build after the plan: same people, same notes, no second vendor.
Most AI consulting ends with a document. The firm that wrote it moves on, and a different vendor, or your own staff, has to turn it into software. Whatever the consultant knew but did not write down is lost in that handoff, and the plan was written by someone who never had to live with it.
Here, the person who assesses the work is the person who builds it. HDS is principal-led, so the plan is written by someone who will be accountable for it running. That changes what goes in it: realistic data work, review gates a person will actually use, and a running cost that has been estimated rather than assumed.
The advice also comes from systems already in production, including retrieval over a law firm's mail and the agent platform HDS delivers its own work on. Scope drives the price more than anything else; what AI automation costs walks through the drivers, and the first conversation is free.
A look at the real work, then a written plan: which tasks AI should take on, what data it reads, where a person approves, how every action is logged, and what it costs to build and run. The plan ends in a build, not a slide deck.
Consulting decides what to build and why. Automation is the build. Here they are one engagement done by the same people, so the plan is written by someone who then has to make it work.
Yes. Smaller teams often see the clearest return, because one or two processes carry most of the wasted hours. If AI is the wrong tool for the problem, the plan says so.
It depends on how many systems the work touches and how much of it can run without a person. The AI automation cost page walks through what drives the number, and the first conversation is free.
Start with a free scoping conversation with Mike Hyams, the person who builds and supports the work.